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Sprint projectOct 25, 2026Hong Kong

Managing Collusion in AI Agents

Ambrose Chan, William Yuen, Michael Choi, Andy Chan, Michael Kit · Team Dragon HK

Submitted to AI Collusion Research Sprint. Sprint projects are early-stage work by participants, not Apart Research publications.

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Autonomous agents can sustain supra-competitive outcomes without explicit agreement. We ask which variables are necessary for collusion, whether information-theoretic statistics can detect it, and which single restriction removes the most collusion at the least cost. In a Calvano-style Bertrand market with independent Q-learners, full observation of rival prices is sufficient for stable positive markups in two-agent markets (up to +0.22). Sequential timing, cheap talk, and side-payments are not necessary once that information is present. With three agents, a compact summary of rival prices restores collusion. A pre-registered comparison separates absolute suppression from cost-adjusted performance: anti-collusion penalties remove the most markup; hiding prices or small price noise is the least-cost way to return average markup near Nash. Residual mutual information does not detect collusion on learned paths (AUROC 0.35-0.65). In a complementary marketplace laboratory, an elicitation ladder produces capture only at an explicit hint, and a trusted-agent pilot shows that speech and action can dissociate. Both rankings, and the negative detection result, are reported.

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Cite this project

@misc{chan2026managing,
  title = {{Managing Collusion in AI Agents}},
  author = {Ambrose Chan and William Yuen and Michael Choi and Andy Chan and Michael Kit},
  year = {2026},
  month = oct,
  note = {Submitted to AI Collusion Research Sprint, an Apart Research Sprint},
  howpublished = {\url{https://apartresearch.com/sprints/projects/managing-collusion-in-ai-agents-bc1r}},
  url = {https://apartresearch.com/sprints/projects/managing-collusion-in-ai-agents-bc1r}
}

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